融合ARIMA模型和MCMC方法的非一致性设计洪水计算OA北大核心CSTPCD

Normal non-stationary hydrological frequency analysis methods are prone to subjectivity when selecting covariates and establishing the functional relationship between statistical parameters and covari-ates.Besides,these methods can only produce design flood estimations,lacking the ability of conducting simultaneous uncertainty analysis.To address the above shortcomings,the ARIMA-MCMC model was established by introducing the time-varying statistical parameters of the fitting period into the MCMC sam-pling process,then the parameter sampling for the non-stationary design flood frequency distribution mod-el was conducted under future climate change conditions.Based on the posterior distribution of the pa-rameters,the design flood frequency was calculated,and the corresponding confidence intervals were de-rived.Taking the Xiaodeshi Station in the Yalong River Basin as an example,the variation of design flood in the station under future climate change conditions was quantatively analyzed by ARIMA-MCMC model.The results show that the convergence effect of the parameter sampling based on ARIMA-MCMC method is excellent,and the values of D for all three scenarios are smaller than the critical value of 5%significance level.Except for the design values with P=0.1%and P=0.05%under scenario SSP2-4.5,the design daily maximum flow in other scenarios increased significantly compared to historical peri-ods,with the increases of 7.1%-10.5%and 13.9%-27.2%under the scenarios of SSP1-2.6 and SSP5-8.5,respectively.The ARIMA-MCMC method established in this study can effectively conduct the non-stationary design flood frequency analysis under changing conditions.